IEEE Access (Jan 2023)
HeartWave: A Multiclass Dataset of Heart Sounds for Cardiovascular Diseases Detection
Abstract
The auscultation of heart sounds has proven advantageous for the early diagnosis of cardiovascular conditions. Various methods have been proposed for the automatic analysis of heart sounds to reduce subjectivity in diagnosis and alleviate physicians’ workload. However, the effectiveness of these methods heavily depends on the amount and quality of the heart sound datasets used and the availability of publicly accessible datasets that include the most common and difficult classes. In this study, we introduce the HeartWave dataset, a comprehensive heart sound dataset comprising recordings from nine distinct classes of the most common heart sounds from all classes and subclasses of cardiovascular diseases, documented, with enough samples, good quality, and well labelled, with a focus on the hard and difficult cases of diagnosis. The dataset includes a total of 1353 recordings of heart sounds. Notably, this dataset includes extremely rare and difficult-to-diagnose classes. In order to establish a reliable reference standard, a team of experienced cardiologists actively participated in the entire annotation process. The length of audio recordings is substantial, allowing for the extraction of multiple heartbeats from a single recording through the use of segmentation techniques. Moreover, our dataset takes into consideration the standard cardiologist practices to enable the capture of specific heart sounds associated with corresponding clinical locations. According to our post analysis of the dataset, the average signal-to-noise ratio of our proposed dataset surpasses that of the widely known PhysioNet/CinC 2016 public dataset by about two folds, ensuring a cleaner acoustic signal. Our proposed dataset provides a valuable resource for training and evaluating machine learning models aimed at automated heart sound classification and diagnosis.
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